On the other hand, deep learning based stereo matching networks [1]}, [2]}, [3]} utilize the stereo constraints to improve efficiency and the output depth quality. These methods simulate the traditional stereo matching process by learning and optimizing the matching cost across the input image pairs in a deterministic manner.
Unlike monocular depth methods which directly map RGB into depth by considering all the monocular cues, stereo matching methods focus on estimating disparity by developing image patch correspondence [4]}. Given the predefined baseline and 1D search space along the epipolar line for image patch matching [5]}, stereo matching produces more accurate depth maps in comparison with monocular methods in general [6]}.
